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A County‐Level Assessment of Ground Water Contamination by Pesticides
Author(s) -
Shukla Sanjay,
Mostaghimi Saied,
Shanholt Ver O.,
Collins Michael C.,
Ross Burton B.
Publication year - 2000
Publication title -
groundwater monitoring and remediation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.677
H-Index - 47
eISSN - 1745-6592
pISSN - 1069-3629
DOI - 10.1111/j.1745-6592.2000.tb00257.x
Subject(s) - environmental science , pesticide , contamination , groundwater , leaching (pedology) , hydrology (agriculture) , vadose zone , atrazine , geographic information system , hydrogeology , soil water , soil science , environmental engineering , remote sensing , geology , agronomy , ecology , geotechnical engineering , biology
Abstract A pesticide screening model was integrated with a geographic information system (GIS) for evaluating the ground water vulnerability to pesticide contamination in Albemarle County, Virginia. The attenuation factor (AF). an index of pesticide mass emission from the vadose zone, was used to evaluate the relative contamination potential of 70 pesticides used in the county. Results for only three pesticides—atrazine, dicamba, and lindane—are discussed in this paper. Spatial (land use, soils, and hydrogeology) and relational (soil and pesticide properties) data layers were combined with the AF model within the GIS environment for spatial computation of AF for actual and 2 m ground water depths. For each pesticide, a ground water vulnerability map with five contamination potential categories (high, medium, low, very low, and unlikely) was generated, based on the spatial distribution of AF for each cell size of 0.27 acre (0.11 ha). To consider the variability in pesticide transport, model simulations were performed for “maximum,”“average,” and “minimum” scenarios of pesticide leaching. Under the average leaching scenario (2 m depth), the three pesticides were found to have very low to low levels of contamination potential in some areas. For maximum leaching scenario (2 m depth), contamination potential of these three pesticides increased to low to medium levels. When actual ground water depth was used, no significant contamination potential was indicated by any of the three pesticides. The modeling approach was evaluated using the data from a limited monitoring study in Albemarle County. Although agreement between the model prediction and actual pesticide detection was observed, extent of the data was not sufficient enough to draw firm conclusions about the model's success in predicting contamination potential. A sensitivity analysis of the methodology revealed that significant uncertainty can be involved in predicting the county‐scale contamination potential. To make better use of this study, it is recommended that a comprehensive pesticide monitoring be undertaken in Albemarle County.